How would your summer season vacation’s pictures look had Edvard Munch painted them? (Maybe it’s higher to not know).
Let’s take a extra comforting instance: How would a pleasant, summarly river panorama look if painted by Katsushika Hokusai?
Fashion switch on pictures isn’t new, however obtained a lift when Gatys, Ecker, and Bethge(Gatys, Ecker, and Bethge 2015) confirmed find out how to efficiently do it with deep studying.
The primary concept is simple: Create a hybrid that may be a tradeoff between the content material picture we need to manipulate, and a model picture we need to imitate, by optimizing for maximal resemblance to each on the identical time.
In case you’ve learn the chapter on neural model switch from Deep Studying with R, it’s possible you’ll acknowledge a number of the code snippets that comply with.
Nonetheless, there is a crucial distinction: This put up makes use of TensorFlow Keen Execution, permitting for an crucial method of coding that makes it simple to map ideas to code.
Identical to earlier posts on keen execution on this weblog, this can be a port of a Google Colaboratory pocket book that performs the identical process in Python.
As typical, please be sure you have the required bundle variations put in. And no want to repeat the snippets – you’ll discover the whole code among the many Keras examples.
Conditions
The code on this put up is dependent upon the newest variations of a number of of the TensorFlow R packages. You possibly can set up these packages as follows:
set up.packages(c("tensorflow", "keras", "tfdatasets"))You also needs to ensure that you’re working the very newest model of TensorFlow (v1.10), which you’ll be able to set up like so:
library(tensorflow)
install_tensorflow()There are extra necessities for utilizing TensorFlow keen execution. First, we have to name tfe_enable_eager_execution() proper at first of this system. Second, we have to use the implementation of Keras included in TensorFlow, quite than the bottom Keras implementation.
Conditions behind us, let’s get began!
Enter pictures
Right here is our content material picture – exchange by a picture of your personal:
# In case you have sufficient reminiscence in your GPU, no must load the photographs
# at such small dimension.
# That is the dimensions I discovered working for a 4G GPU.
img_shape <- c(128, 128, 3)
content_path <- "isar.jpg"
content_image <- image_load(content_path, target_size = img_shape[1:2])
content_image %>%
image_to_array() %>%
`/`(., 255) %>%
as.raster() %>%
plot()
And right here’s the model mannequin, Hokusai’s The Nice Wave off Kanagawa, which you’ll be able to obtain from Wikimedia Commons:
We create a wrapper that hundreds and preprocesses the enter pictures for us.
As we will probably be working with VGG19, a community that has been educated on ImageNet, we have to rework our enter pictures in the identical method that was used coaching it. Later, we’ll apply the inverse transformation to our mixture picture earlier than displaying it.
load_and_preprocess_image <- perform(path) {
img <- image_load(path, target_size = img_shape[1:2]) %>%
image_to_array() %>%
k_expand_dims(axis = 1) %>%
imagenet_preprocess_input()
}
deprocess_image <- perform(x) {
x <- x[1, , ,]
# Take away zero-center by imply pixel
x[, , 1] <- x[, , 1] + 103.939
x[, , 2] <- x[, , 2] + 116.779
x[, , 3] <- x[, , 3] + 123.68
# 'BGR'->'RGB'
x <- x[, , c(3, 2, 1)]
x[x > 255] <- 255
x[x < 0] <- 0
x[] <- as.integer(x) / 255
x
}Setting the scene
We’re going to use a neural community, however we gained’t be coaching it. Neural model switch is a bit unusual in that we don’t optimize the community’s weights, however again propagate the loss to the enter layer (the picture), to be able to transfer it within the desired course.
We will probably be focused on two sorts of outputs from the community, comparable to our two targets.
Firstly, we need to maintain the mix picture much like the content material picture, on a excessive degree. In a convnet, higher layers map to extra holistic ideas, so we’re choosing a layer excessive up within the graph to match outputs from the supply and the mix.
Secondly, the generated picture ought to “appear to be” the model picture. Fashion corresponds to decrease degree options like texture, shapes, strokes… So to match the mix towards the model instance, we select a set of decrease degree conv blocks for comparability and mixture the outcomes.
content_layers <- c("block5_conv2")
style_layers <- c("block1_conv1",
"block2_conv1",
"block3_conv1",
"block4_conv1",
"block5_conv1")
num_content_layers <- size(content_layers)
num_style_layers <- size(style_layers)
get_model <- perform() {
vgg <- application_vgg19(include_top = FALSE, weights = "imagenet")
vgg$trainable <- FALSE
style_outputs <- map(style_layers, perform(layer) vgg$get_layer(layer)$output)
content_outputs <- map(content_layers, perform(layer) vgg$get_layer(layer)$output)
model_outputs <- c(style_outputs, content_outputs)
keras_model(vgg$enter, model_outputs)
}Losses
When optimizing the enter picture, we are going to contemplate three kinds of losses. Firstly, the content material loss: How totally different is the mix picture from the supply? Right here, we’re utilizing the sum of the squared errors for comparability.
content_loss <- perform(content_image, goal) {
k_sum(k_square(goal - content_image))
}Our second concern is having the types match as carefully as doable. Fashion is usually operationalized because the Gram matrix of flattened function maps in a layer. We thus assume that model is expounded to how maps in a layer correlate with different.
We due to this fact compute the Gram matrices of the layers we’re focused on (outlined above), for the supply picture in addition to the optimization candidate, and examine them, once more utilizing the sum of squared errors.
gram_matrix <- perform(x) {
options <- k_batch_flatten(k_permute_dimensions(x, c(3, 1, 2)))
gram <- k_dot(options, k_transpose(options))
gram
}
style_loss <- perform(gram_target, mixture) {
gram_comb <- gram_matrix(mixture)
k_sum(k_square(gram_target - gram_comb)) /
(4 * (img_shape[3] ^ 2) * (img_shape[1] * img_shape[2]) ^ 2)
}Thirdly, we don’t need the mix picture to look overly pixelated, thus we’re including in a regularization element, the whole variation within the picture:
total_variation_loss <- perform(picture) {
y_ij <- picture[1:(img_shape[1] - 1L), 1:(img_shape[2] - 1L),]
y_i1j <- picture[2:(img_shape[1]), 1:(img_shape[2] - 1L),]
y_ij1 <- picture[1:(img_shape[1] - 1L), 2:(img_shape[2]),]
a <- k_square(y_ij - y_i1j)
b <- k_square(y_ij - y_ij1)
k_sum(k_pow(a + b, 1.25))
}The tough factor is find out how to mix these losses. We’ve reached acceptable outcomes with the next weightings, however be happy to mess around as you see match:
content_weight <- 100
style_weight <- 0.8
total_variation_weight <- 0.01Get mannequin outputs for the content material and magnificence pictures
We’d like the mannequin’s output for the content material and magnificence pictures, however right here it suffices to do that simply as soon as.
We concatenate each pictures alongside the batch dimension, cross that enter to the mannequin, and get again a listing of outputs, the place each aspect of the record is a 4-d tensor. For the model picture, we’re within the model outputs at batch place 1, whereas for the content material picture, we’d like the content material output at batch place 2.
Within the beneath feedback, please be aware that the sizes of dimensions 2 and three will differ when you’re loading pictures at a distinct dimension.
get_feature_representations <-
perform(mannequin, content_path, style_path) {
# dim == (1, 128, 128, 3)
style_image <-
load_and_process_image(style_path) %>% k_cast("float32")
# dim == (1, 128, 128, 3)
content_image <-
load_and_process_image(content_path) %>% k_cast("float32")
# dim == (2, 128, 128, 3)
stack_images <- k_concatenate(record(style_image, content_image), axis = 1)
# size(model_outputs) == 6
# dim(model_outputs[[1]]) = (2, 128, 128, 64)
# dim(model_outputs[[6]]) = (2, 8, 8, 512)
model_outputs <- mannequin(stack_images)
style_features <-
model_outputs[1:num_style_layers] %>%
map(perform(batch) batch[1, , , ])
content_features <-
model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)] %>%
map(perform(batch) batch[2, , , ])
record(style_features, content_features)
}Computing the losses
On each iteration, we have to cross the mix picture by the mannequin, receive the model and content material outputs, and compute the losses. Once more, the code is extensively commented with tensor sizes for straightforward verification, however please needless to say the precise numbers presuppose you’re working with 128×128 pictures.
compute_loss <-
perform(mannequin, loss_weights, init_image, gram_style_features, content_features) {
c(style_weight, content_weight) %<-% loss_weights
model_outputs <- mannequin(init_image)
style_output_features <- model_outputs[1:num_style_layers]
content_output_features <-
model_outputs[(num_style_layers + 1):(num_style_layers + num_content_layers)]
# model loss
weight_per_style_layer <- 1 / num_style_layers
style_score <- 0
# dim(style_zip[[5]][[1]]) == (512, 512)
style_zip <- transpose(record(gram_style_features, style_output_features))
for (l in 1:size(style_zip)) {
# for l == 1:
# dim(target_style) == (64, 64)
# dim(comb_style) == (1, 128, 128, 64)
c(target_style, comb_style) %<-% style_zip[[l]]
style_score <- style_score + weight_per_style_layer *
style_loss(target_style, comb_style[1, , , ])
}
# content material loss
weight_per_content_layer <- 1 / num_content_layers
content_score <- 0
content_zip <- transpose(record(content_features, content_output_features))
for (l in 1:size(content_zip)) {
# dim(comb_content) == (1, 8, 8, 512)
# dim(target_content) == (8, 8, 512)
c(target_content, comb_content) %<-% content_zip[[l]]
content_score <- content_score + weight_per_content_layer *
content_loss(comb_content[1, , , ], target_content)
}
# complete variation loss
variation_loss <- total_variation_loss(init_image[1, , ,])
style_score <- style_score * style_weight
content_score <- content_score * content_weight
variation_score <- variation_loss * total_variation_weight
loss <- style_score + content_score + variation_score
record(loss, style_score, content_score, variation_score)
}Computing the gradients
As quickly as now we have the losses, acquiring the gradients of the general loss with respect to the enter picture is only a matter of calling tape$gradient on the GradientTape. Observe that the nested name to compute_loss, and thus the decision of the mannequin on our mixture picture, occurs contained in the GradientTape context.
compute_grads <-
perform(mannequin, loss_weights, init_image, gram_style_features, content_features) {
with(tf$GradientTape() %as% tape, {
scores <-
compute_loss(mannequin,
loss_weights,
init_image,
gram_style_features,
content_features)
})
total_loss <- scores[[1]]
record(tape$gradient(total_loss, init_image), scores)
}Coaching part
Now it’s time to coach! Whereas the pure continuation of this sentence would have been “… the mannequin,” the mannequin we’re coaching right here isn’t VGG19 (that one we’re simply utilizing as a device), however a minimal setup of simply:
- a
Variablethat holds our to-be-optimized picture - the loss capabilities we outlined above
- an optimizer that can apply the calculated gradients to the picture variable (
tf$prepare$AdamOptimizer)
Under, we get the model options (of the model picture) and the content material function (of the content material picture) simply as soon as, then iterate over the optimization course of, saving the output each 100 iterations.
In distinction to the unique article and the Deep Studying with R e-book, however following the Google pocket book as an alternative, we’re not utilizing L-BFGS for optimization, however Adam, as our objective right here is to offer a concise introduction to keen execution.
Nonetheless, you would plug in one other optimization technique when you wished, changing
optimizer$apply_gradients(record(tuple(grads, init_image)))
by an algorithm of your selection (and naturally, assigning the results of the optimization to the Variable holding the picture).
run_style_transfer <- perform(content_path, style_path) {
mannequin <- get_model()
stroll(mannequin$layers, perform(layer) layer$trainable = FALSE)
c(style_features, content_features) %<-%
get_feature_representations(mannequin, content_path, style_path)
# dim(gram_style_features[[1]]) == (64, 64)
gram_style_features <- map(style_features, perform(function) gram_matrix(function))
init_image <- load_and_process_image(content_path)
init_image <- tf$contrib$keen$Variable(init_image, dtype = "float32")
optimizer <- tf$prepare$AdamOptimizer(learning_rate = 1,
beta1 = 0.99,
epsilon = 1e-1)
c(best_loss, best_image) %<-% record(Inf, NULL)
loss_weights <- record(style_weight, content_weight)
start_time <- Sys.time()
global_start <- Sys.time()
norm_means <- c(103.939, 116.779, 123.68)
min_vals <- -norm_means
max_vals <- 255 - norm_means
for (i in seq_len(num_iterations)) {
# dim(grads) == (1, 128, 128, 3)
c(grads, all_losses) %<-% compute_grads(mannequin,
loss_weights,
init_image,
gram_style_features,
content_features)
c(loss, style_score, content_score, variation_score) %<-% all_losses
optimizer$apply_gradients(record(tuple(grads, init_image)))
clipped <- tf$clip_by_value(init_image, min_vals, max_vals)
init_image$assign(clipped)
end_time <- Sys.time()
if (k_cast_to_floatx(loss) < best_loss) {
best_loss <- k_cast_to_floatx(loss)
best_image <- init_image
}
if (i %% 50 == 0) {
glue("Iteration: {i}") %>% print()
glue(
"Complete loss: {k_cast_to_floatx(loss)},
model loss: {k_cast_to_floatx(style_score)},
content material loss: {k_cast_to_floatx(content_score)},
complete variation loss: {k_cast_to_floatx(variation_score)},
time for 1 iteration: {(Sys.time() - start_time) %>% spherical(2)}"
) %>% print()
if (i %% 100 == 0) {
png(paste0("style_epoch_", i, ".png"))
plot_image <- best_image$numpy()
plot_image <- deprocess_image(plot_image)
plot(as.raster(plot_image), predominant = glue("Iteration {i}"))
dev.off()
}
}
}
glue("Complete time: {Sys.time() - global_start} seconds") %>% print()
record(best_image, best_loss)
}Able to run
Now, we’re prepared to begin the method:
c(best_image, best_loss) %<-% run_style_transfer(content_path, style_path)In our case, outcomes didn’t change a lot after ~ iteration 1000, and that is how our river panorama was wanting:
… positively extra inviting than had it been painted by Edvard Munch!
Conclusion
With neural model switch, some fiddling round could also be wanted till you get the outcome you need. However as our instance reveals, this doesn’t imply the code must be sophisticated. Moreover to being simple to understand, keen execution additionally allows you to add debugging output, and step by the code line-by-line to test on tensor shapes.
Till subsequent time in our keen execution sequence!
